R: Principal component analysis

acp {amap} | R Documentation

## Principal component analysis

### Description

Principal component analysis

### Usage

    acp(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))
    pca(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))
    ## S3 method for class 'acp'
    print(x, ...)

### Arguments

`x` | Matrix / data frame
`center` | a logical value indicating whether we center data
`reduce` | a logical value indicating whether we "reduce" data i.e. divide each column by standard deviation
`wI,wV` | weigth vector for individuals / variables
`...` | arguments to be passed to or from other methods.

### Details

This function offer a variant of `princomp` and `prcomp` functions, with a slightly different graphic representation (see `plot.acp`).

### Value

An object of class acp The object is a list with components:

`sdev` | the standard deviations of the principal components.
`loadings` | the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class `"loadings"`: see `loadings` for its `print` method.
`scores` | if `scores = TRUE`, the scores of the supplied data on the principal components.
`eig` | Eigen values

### Author(s)

Antoine Lucas

### See Also

plot.acp,acpgen, princomp

### Examples

    data(lubisch)
    lubisch <- lubisch[,-c(1,8)]
    p <- acp(lubisch)
    plot(p)

[Package amap version 0.8-18 Index]